• Journal of Computational Design and Engineering
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• 제2권2호
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• pp.79-87
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• 2015

Surface segmentation and edge feature lines extraction from fractured fragments of relics are essential steps for computer assisted restoration of fragmented relics. As these fragments were heavily eroded, it is a challenging work to segment surface and extract edge feature lines. This paper presents a novel method to segment surface and extract edge feature lines from triangular meshes of irregular fractured fragments. Firstly, a rough surface segmentation is accomplished by using a clustering algorithm based on the vertex normal vector. Secondly, in order to differentiate between original and fracture faces, a novel integral invariant is introduced to compute the surface roughness. Thirdly, an accurate surface segmentation is implemented by merging faces based on face normal vector and roughness. Finally, edge feature lines are extracted based on the surface segmentation. Some experiments are made and analyzed, and the results show that our method can achieve surface segmentation and edge extraction effectively.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.643-646
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• 2006

This paper addresses the approach to extract linear features from satellite imagery using an efficient segmentation method. The extraction of linear features from satellite images has been the main concern of many scientists. There is a need to develop a more capable and cost effective method for the Iranian map revision tasks. The conventional approaches for producing, maintaining, and updating GIS map are time consuming and costly process. Hence, this research is intended to investigate how to obtain linear features from SPOT satellite imagery. This was accomplished using a discontinuity-based segmentation technique that encompasses four stages: low level bottom-up, middle level bottom-up, edge thinning and accuracy assessment. The first step is geometric correction and noise removal using suitable operator. The second step includes choosing the appropriate edge detection method, finding its proper threshold and designing the built-up image. The next step is implementing edge thinning method using mathematical morphology technique. Lastly, the geometric accuracy assessment task for feature extraction as well as an assessment for the built-up result has been carried out. Overall, this approach has been applied successfully for linear feature extraction from SPOT image.

• 대한원격탐사학회지
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• 제20권3호
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• pp.207-214
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• 2004

This study focused on providing a methodology to utilize temporal information obtained from remotely sensed data for monitoring a wide variety of targets on the earth's surface. Generally, a methodology in understanding of global changes is composed of mapping, quantifying, and monitoring changes in the physical characteristics of land cover. The selected processing and analysis technique affects the quality of the obtained information. In this research, feature extraction methodology is proposed based on segmentation. It requires a series of processing of multitempotal images: preprocessing of geometric and radiometric correction, image subtraction/thresholding technique, and segmentation/thresholding. It results in the mapping of the change-detected areas. Here, the appropriate methods are studied for each step and especially, in segmentation process, a method to delineate the exact boundaries of features is investigated in multiresolution framework to reduce computational complexity for multitemporal images of large size.

• 한국군사과학기술학회지
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• 제20권1호
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• pp.40-47
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• 2017

To classify the vehicle targets automatically using thermal images, there are usually two main categories of feature extraction method, local and shape feature extraction methods. Since thermal images have less texture information than color images, the shape feature extraction method is useful when the segmentation results are correct. However, if there are some errors in target segmentation, the shape feature may contain some errors, then the classification accuracy can be decreased. To overcome these problems, in this paper, we propose the segmentation reliability estimation method for target classification. The segmentation reliability can be estimated by using the difference information of average intensities and edge energies between the target and the background area. The estimated segmentation reliability is applied in the decision level fusion method of classification results using both shape and local features. Experiment results using the thermal images of the vehicle targets (main battle tank, armored personnel carrier, military truck, and an estate car) show that the proposed classification method and the segmentation reliability estimation method have a good performance in classification accuracy.

• 한국멀티미디어학회논문지
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• 제22권1호
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• pp.27-34
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• 2019

In this paper, a new type of lip feature is proposed as distance metric in CIELUV color system. The performance of the proposed feature was tested on face image database, Helen dataset from University of Illinois. The test processes consists of three steps. The first step is feature extraction and second step is principal component analysis for the optimal projection of a feature vector. The final step is Otsu's threshold for a two-class problem. The performance of the proposed feature was better than conventional features. Performance metrics for the evaluation are OverLap and Segmentation Error. Best performance for the proposed feature was OverLap of 65% and 59 % of segmentation error. Conventional methods shows 80~95% for OverLap and 5~15% of segmentation error usually. In conventional cases, the face database is well calibrated and adjusted with the same background and illumination for the scene. The Helen dataset used in this paper is not calibrated or adjusted at all. These images are gathered from internet and therefore, there are no calibration and adjustment.

• Journal of Information Processing Systems
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• 제9권1호
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• pp.117-140
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• 2013

Hindi is the most widely spoken language in India, with more than 300 million speakers. As there is no separation between the characters of texts written in Hindi as there is in English, the Optical Character Recognition (OCR) systems developed for the Hindi language carry a very poor recognition rate. In this paper we propose an OCR for printed Hindi text in Devanagari script, using Artificial Neural Network (ANN), which improves its efficiency. One of the major reasons for the poor recognition rate is error in character segmentation. The presence of touching characters in the scanned documents further complicates the segmentation process, creating a major problem when designing an effective character segmentation technique. Preprocessing, character segmentation, feature extraction, and finally, classification and recognition are the major steps which are followed by a general OCR. The preprocessing tasks considered in the paper are conversion of gray scaled images to binary images, image rectification, and segmentation of the document's textual contents into paragraphs, lines, words, and then at the level of basic symbols. The basic symbols, obtained as the fundamental unit from the segmentation process, are recognized by the neural classifier. In this work, three feature extraction techniques-: histogram of projection based on mean distance, histogram of projection based on pixel value, and vertical zero crossing, have been used to improve the rate of recognition. These feature extraction techniques are powerful enough to extract features of even distorted characters/symbols. For development of the neural classifier, a back-propagation neural network with two hidden layers is used. The classifier is trained and tested for printed Hindi texts. A performance of approximately 90% correct recognition rate is achieved.

• 정보처리학회논문지B
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• 제13B권4호
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• pp.401-408
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• 2006

영상신호의 수직축 및 수평축 화소 성분 분석을 통해서, 영상 내부에 존재하는 각 영역의 군집적 특성을 통계 및 영역적으로 처리 분류함으로써 필요한 특징을 추출할 수 있는 새로운 형태의 영역분할처리 알고리즘을 제시한다. 종래의 점처리나 면처리 방식에 비해 이 방식은 수평축과 수직축 상에서의 연속적인 선처리 방식이라고 할 수 있다. 영상을 구성하는 영역간 경계가 암시적으로 구분되어 있으나, 명시적으로는 불투명하고, 영상 특성의 분기점 또한 불명확하고 중복되어 있음으로 인하여 문턱치처리나 분기점처리로 그 영역간 특정을 분할, 추출하기가 곤란한 경우에 이 방식은 우수한 효과가 있다. 수평축 및 수직축 선처리를 통해 각 영역들의 특성들을 군집으로 처리한 다음 처리한 축과 수직 방향으로 축차적 적응진행처리한다. 그 결과 영상 내 각 영역은 화소값의 중복에도 불구하고 하나의 군집으로 자리매김하면서 군집 고유의 화소 값을 갖는다. 그리고 처리후 영상은 각 군집에 부여한 새로운 화소값으로 변환함으로 필요한 특정이 추출된다. 이 방식은 특히 영역 분할을 통해 시각적 효과를 극대화시킬 필요가 있는 경동맥 초음파 의료영상에서 우수한 결과를 보였다.

• 대한의용생체공학회:의공학회지
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• 제22권3호
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• pp.231-239
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• 2001

This paper proposes a region segmenting method for the Pap-smear image. The proposed method uses a pixel classifier based on neural network, which consists of four stages : preprocessing, feature extraction, region segmentation and postprocessing. In the preprocessing stage, brightness value is normalized by histogram stretching. In the feature extraction stage, total 36 features are extracted from $3{\times}3$ or $5{\times}5$ window. In the region segmentation stage, each pixel which is associated with 36 features, is classified into 3 groups : nucleus, cytoplasm and background. The backpropagation network is used for classification. In the postprocessing stage, the pixel, which have been rejected by the above classifier, are re-classified by the relaxation algorithm. It has been shown experimentally that the proposed method finds the nucleus region accurately and it can find the cytoplasm region too.

• 한국게임학회 논문지
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• 제11권2호
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• pp.161-166
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• 2011

본 논문에서는 동작인식 위한 정확한 배경 분할 및 특징점 추출 방법을 제안한다. 배경 분할 과정에서는 먼저, HSV 입력 이미지를 RGB 색상 공간에서 HSV 색상 공간으로 변환한 뒤, H와 S 값에 대한 두 개의 임계치를 사용하여 살색 영역을 분할, 프레임간의 차영상을 이용하여 움직임이 있는 영역을 추출한다. 차영상에서 발생하는 잔상 영역을 제거하기 위하여 헤시안 어파인 영역 검출기를 적용하고, 잡음이 제거된 차 영상과 살색 영역의 이진화 영상을 이용하여 사람의 동작이 나타나는 영역을 분할한다. 특징점 추출 과정은 전체 영상을 블록 단위로 나눠서 각 블록 안에서 분할된 영상에 포함되는 픽셀들의 중점을 구하여 특징점을 추출한다. 실험결과 복잡한 환경에서도 정확한 배경 분할과 사용자 동작을 대표하는 특징점 추출이 약 12 fps로 가능함을 알 수 있었다.

• 한국정보통신학회논문지
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• 제10권4호
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• pp.674-679
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• 2006

본 논문에서는 3차원 손 특징 데이터를 이용한 동작 인식 시스템을 제안하고자 한다. 제안된 시스템은 3차원 센서에 의해 조밀한 범위의 영상을 생성하여 손 동작에 대한 3차원 특징을 추출하여 손 동작을 분류한다. 또한 다양한 조명과 배경하에서의 손을 견실하게 분할하고 색상 정보와 상관이 없어 수화와 같은 복잡한 손 동작에 대해서도 견실한 인식능력을 나타낼 수가 있다. 제안된 방법의 전체적인 순서는 3차원 영상 획득, 팔 분할, 손과 팔목 분할, 손 자세 추정, 3차원 특징 추출, 그리고 동작 분류로 구성되어 있고, 수화 자세에 대한 인식 실험으로 제안된 시스템의 효율성을 입증하였다.